机器人感知与控制关键技术及其智能制造应用

王耀南; 江一鸣; 姜娇; 张辉; 谭浩然; 彭伟星; 吴昊天; 曾凯

doi:10.16383/j.aas.c220995

机器人感知与控制关键技术及其智能制造应用

doi: 10.16383/j.aas.c220995

王耀南^{1, 2,},
江一鸣^{1, 3,},
姜娇^{1, 2,},
张辉^{1, 3,},
谭浩然^{1, 2,},
彭伟星^{1, 2,},
吴昊天^{1, 2,},
曾凯^{1, 2,}

1.
机器人视觉感知与控制技术国家工程研究中心长沙 410082
2.
湖南大学电气与信息工程学院长沙 410082
3.
湖南大学机器人学院长沙 410082

基金项目: 国家自然科学基金(62293510, 62027810, 62003136, 62103138, 92148204), 国家重点研发计划(2021YFB1714700, 2021ZD0114503), 湖南省创新型省份建设专项资金支持(2021GK1010, 2021JJ10025), 中国博士后科学基金(BX2021098), 长沙市自然科学基金(kq2014060), 广东省人工智能与数字经济实验室(深圳)开放研究基金(GML-KF-22-14), 湘江实验室重大项目(22xj01006)资助

详细信息

作者简介:
王耀南：中国工程院院士, 湖南大学电气与信息工程学院教授. 1995年获湖南大学博士学位. 主要研究方向为机器人学, 智能控制和图像处理. E-mail: yaonan@hnu.edu.cn

江一鸣：湖南大学机器人学院副教授, 机器人视觉感知与控制技术国家工程研究中心副研究员. 主要研究方向为多机器人协同控制及应用. 本文通信作者. E-mail: ymjiang@hnu.edu.cn

姜娇：湖南大学电气与信息工程学院博士研究生. 2020年获中国矿业大学学士学位. 主要研究方向为机器人视觉感知与控制. E-mail: jiangjiao@hnu.edu.cn

张辉：湖南大学机器人学院教授. 主要研究方向为机器视觉, 图像处理和机器人控制. E-mail: zhanghuihby@126.com

谭浩然：湖南大学电气与信息工程学院副教授. 主要研究方向为机器人智能控制与网络化控制系统. E-mail: tanhaoran@hnu.edu.cn

彭伟星：湖南大学电气与信息工程学院博士后. 主要研究方向为复杂零部件的多机器人协同自主测量技术及应用. E-mail: weixing_peng@hnu.edu.cn

吴昊天：机器人视觉感知与控制技术国家工程研究中心助理研究员. 主要研究方向为机器人三维测量, 点云处理, 三维重建. E-mail: wuhaotian@hnu.edu.cn

曾凯：湖南大学电气与信息工程学院助理研究员. 主要研究方向为机器人三维环境感知. E-mail: zkwalt@hnu.edu.cn

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出版历程
- 收稿日期: 2022-12-26
- 录用日期: 2023-03-12
- 网络出版日期: 2023-03-22
- 刊出日期: 2023-03-20

Key Technologies of Robot Perception and Control and Its Intelligent Manufacturing Applications

WANG Yao-Nan^{1, 2
,},
JIANG Yi-Ming^{1, 3
,},
JIANG Jiao^{1, 2
,},
ZHANG Hui^{1, 3
,},
TAN Hao-Ran^{1, 2
,},
PENG Wei-Xing^{1, 2
,},
WU Hao-Tian^{1, 2
,},
ZENG Kai^{1, 2
,}

1.
National Engineering Research Center of Robot Visual Perception and Control Technology, Changsha 410082
2.
College of Electrical and Information Engineering, Hunan University, Changsha 410082
3.
School of Robotics, Hunan University, Changsha 410082

Funds: Supported by National Natural Science Foundation of China (62293510, 62027810, 62003136, 62103138, 92148204), National Key Research and Development Program of China (2021YFB1714700, 2021ZD0114503), the Special Funding Support for the Construction of Innovative Provinces of Hunan Province (2021GK1010, 2021JJ10025), the China Postdoctoral Science Foundation (BX2021098), Changsha Municipal Natural Science Foundation (kq2014060), the Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) (GML-KF-22-14), and Major Project of Xiangjiang Laboratory (22xj01006)

More Information

Author Bio:
WANG Yao-Nan　Academician at Chinese Academy of Engineering, professor at the College of Electrical and Information Engineering, Hunan University. He received his Ph.D. degree from Hunan University in 1995. His research interest covers robotics, intelligent control, and image processing

JIANG Yi-Ming　Associate professor at the School of Robotics, Hunan University, associate research fellow at the National Engineering Research Center of Robot Visual Perception and Control Technology. His research interest covers multiple robots cooperative control and their application. Corresponding author of this paper

JIANG Jiao　Ph.D. candidate at the College of Electrical and Information Engineering, Hunan University. She received her bachelor degree from China University of Mining and Technology in 2020. Her research interest covers robot vision perception and control

ZHANG Hui　Professor at the School of Robotics, Hunan University. His research interest covers machine vision, image processing, and robot control

TAN Hao-Ran　Associate professor at the College of Electrical and Information Engineering, Hunan University. His research interest covers robot intelligent control and networked control systems

PENG Wei-Xing　Postdoctor at the College of Electrical and Information Engineering, Hunan University. His research interest covers multi-robot collaborative active measurement of complex parts and their application

WU Hao-Tian　Assistant researcher at the National Engineering Research Center of Robot Visual Perception and Control Technology. His research interest covers robotic 3D measurement, point clouds alignment, and 3D reconstruction

ZENG Kai　Assistant researcher at the College of Electrical and Information Engineering, Hunan University. His main research interest is robot 3D environment perception

摘要

摘要: 智能机器人在服务国家重大需求, 引领国民经济发展和保障国防安全中起到重要作用, 被誉为“制造业皇冠顶端的明珠”. 随着新一轮工业革命的到来, 世界主要工业国家都开始加快机器人技术的战略部署. 而智能机器人作为智能制造的重要载体, 在深入实施制造强国战略, 推动制造业的高端化、智能化、绿色化过程中将发挥重要作用. 本文从智能机器人的感知与控制等关键技术的视角出发, 重点阐述了机器人的三维环境感知、点云配准、位姿估计、任务规划、多机协同、柔顺控制、视觉伺服等共性关键技术的国内外发展现状. 然后, 以复杂曲面机器人三维测量、复杂部件机器人打磨、机器人力控智装配等机器人智能制造系统为例, 阐述了机器人的智能制造的应用关键技术, 并介绍了工程机械智能化无人工厂、无菌化机器人制药生产线等典型案例. 最后探讨了智能制造机器人的发展趋势和所面临的挑战.
- 机器人 /
- 智能制造 /
- 视觉感知 /
- 智能控制 /
- 系统应用 /
- 信息物理系统
Abstract: Intelligent robots play an important role in serving the country＇s major demands, leading the development of the national economy and ensuring national defense security, which are known as “the top of the crown jewel in the manufacturing”. With the advent of a new round of industrial revolution, major industrial countries have begun to accelerate the deployment of robot technology. As an important carrier of intelligent manufacturing, intelligent robots can play an important role in the in-depth implementation of the manufacturing power strategy and the promotion of high-end, intelligent, and green manufacturing. From the perspective of key technologies of perception and control of intelligent robots, this paper briefly reviews the key technologies of robots such as 3D environment perception, point cloud registration, pose estimation, task planning, multi-machine collaboration, compliant control, and visual servoing. Then, the paper reviews the key technologies for the applications of robot in the intelligent manufacturing, such as robot three-dimensional measurement of complex curved surfaces, robot grinding of complex parts, and robot force-controlled intelligent assembly. Typical cases such as bacterial chemical robot pharmaceutical production line are introduced as well. Finally, the development trend and challenges of intelligent manufacturing robots are discussed.
- Robot /
- intelligent manufacturing /
- visual perception /
- intelligent control /
- system applications /
- cyber-physical system

HTML全文

图 1 机器人智能感知关键技术及应用

Fig. 1 Key technologies and applications for robot intelligent perception

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图 2 基于超像素分割的双目立体匹配方法

Fig. 2 Binocular stereo matching method based on super pixel segmentation

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图 3 基于CNN网络的6D位姿估计算法PoseCNN

Fig. 3 PoseCNN a 6D pose estimation algorithm based on CNN network

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图 4 机器人规划与控制关键技术

Fig. 4 Key technologies for robot planning and control

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图 5 机器人视觉伺服方案

Fig. 5 Robot vision servo solution

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图 6 工业领域三维测量技术的发展时间轴

Fig. 6 Timeline of the development of 3D measurement technology in industry

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图 7 工业三维测量系统^[115]

Fig. 7 Industrial 3D measurement systems^[115]

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图 8 复杂结构零部件三维测量系统^{[37, 117]}

Fig. 8 3D measurement system for complex structural components^{[37, 117]}

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图 9 铸件表面清理机器人现场打磨作业场景

Fig. 9 Casting surface cleaning robot field grinding operation scenery

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图 10 工程机械机器人智能制造车间

Fig. 10 Construction machinery robot intelligent manufacturing laboratory

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图 11 高端制药机器人视觉检测控制关键技术与装备

Fig. 11 High-end pharmaceutical robot vision inspection control key technology and equipment

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图 12 “云−边−端”融合的机器人智能制造模式

Fig. 12 Intelligent robot manufacturing based on cloud-edge-device integration

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图 13 集群机器人协同制造新模式

Fig. 13 A new pattern of collaborative manufacturing with clustered robots

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